Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel

• Published in: Journal of physics. Conference series 2024-06, Vol.2767 (3), p.032049
• Main Authors: Hulsman, P., Howland, M., Göçmen, T., Petrović, V.
• Format: Article
• Language: English
• Subjects: Algorithms; Closed loops; Data points; Error correction; Machine learning; Mathematical models; Power gain; Steering; Turbines; Wind direction; Wind farms; Wind power; Wind tunnels; Yaw
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2767/3/032049

This paper introduces a framework to assess a hybrid data-driven wake steering strategy combining the advantages of an analytical model for the initial data gathering together with a data-driven model for modelling error correction. The main building blocks of the control strategy (required input domain, data gathering and model generation) are evaluated within the wind tunnel, utilising a newly developed setup that can simulate dynamic wind direction variations and which was specifically designed to examine wake steering control strategies. The control strategy employs a machine-learning algorithm to generate a turbine and wake model based on the measurement data, allowing this approach to be extrapolated for an arbitrarily large wind farm. The machine-learning model is used to estimate the optimal yaw angle in a closed-loop format. To implement the developed control strategy, steady measurements are analysed first to determine the required input variables. This is followed by an investigation of the potential power gain and the impact of utilising a grid-search procedure. The procedure minimises adverse conditions as it collects the required data points, still achieving a power gain.